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Markus Sommer

Determining whether a cancer patient responds to immunotherapy, detecting contamination in food and water, measuring mitochondrial membrane potential: scientists depend on flow cytometry.

Code Video Cerebral Valley

Core trick

Unmixes spectral FCS signal, clusters cells locally, and exports clusters as editable FlowJo gate paths without uploading data.

Tags

healthscience

Description

Determining whether a cancer patient responds to immunotherapy, detecting contamination in food and water, measuring mitochondrial membrane potential: scientists depend on flow cytometry. Experiments can measure thousands of cells; the bottleneck is analysis, where scientists gate by hand or simply give up on 40-marker spectral panels. The tools that could help are expensive, cloud-based, or need years of programming experience, leaving immunologists with sensitive patient data and limited coding experience stuck. WakaFlockaFlow closes that gap: a free, no-code tool that installs in one command and runs on the lab's own machine. Nothing leaves the building. Point it at raw files and it unmixes spectral signal, names cell populations, scores functional state, pools cohorts onto one map, and compares groups statistically. It exports gating paths to FlowJo as real, editable gates, fitting existing workflows rather than replacing them. Validation runs three ways. On synthetic ground truth it recovers signal, returns zero on a null cohort, and scales properly with effect size. On the Bodenmiller benchmark (real, hand-gated PBMCs) it reproduces the B-cell activation response (pS6 up, p 1.2e-6) and caught a real, documented annotation bug in the data. Live on PyPI today.
Markusjsommer/WakaFlockaFlow★ 10 forksPythonAGPL-3.0Created Jul 8, 2026 · last push Jul 13, 2026

README excerpt

# WakaFlockaFlow, a no-code tool for automated spectral flow cytometry analysis

[![License: AGPL v3](https://img.shields.io/badge/License-AGPL_v3-blue.svg)](https://www.gnu.org/licenses/agpl-3.0)
[![Python 3.12+](https://img.shields.io/badge/python-3.12+-blue.svg)](https://www.python.org/downloads/)
[![Runs locally](https://img.shields.io/badge/data-stays%20local-brightgreen.svg)](#privacy)
[![Pypi Release](https://badge.fury.io/py/wakaflockaflow.svg)](https://pypi.org/project/wakaflockaflow/)
[![PyPI Downloads](https://static.pepy.tech/personalized-badge/wakaflockaflow?period=total&units=INTERNATIONAL_SYSTEM&left_color=GREY&right_color=GREEN&left_text=downloads)](https://pepy.tech/projects/wakaflockaflow)

WakaFlockaFlow takes spectral flow cytometry FCS files and, without any coding,
produces named cell populations with counts, frequencies, marker profiles, and
functional-state calls, then compares them across an experimental cohort with a
statistic. It runs a post-acquisition workflow behind a browser interface:
spectral unmixing, transformation, automated population identification with
cell-type annotation (FlowSOM + UMAP), multi-sample cohort analysis on one shared
embedding, differential abundance and state testing, and explainable gating
paths. It exports a reproducibility bundle for every run. Cross-acquisition
batch-correction engines (CytoNorm, ComBat) are included; a guided interface for
them is on the roadmap.

It covers four of the five things a lab asks of post

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